Adaptive Quantum Kernel Learning for Non-Stationary Data

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper addresses the challenges posed by non-stationary data distributions in kernel-based machine learning. Traditional kernel methods often falter when the underlying data evolves over time, requiring frequent retraining and potentially leading to suboptimal performance. We propose an adaptive quantum kernel learning algorithm designed to mitigate this issue. The core idea is to dynamically adjust the kernel parameters based on observed changes in the data distribution. This adaptation is achieved by leveraging quantum interference, which allows the algorithm to identify and capitalize on evolving patterns within the data. Our approach combines the strengths of kernel methods – their ability to capture complex relationships in data – with the potential of quantum computing for enhanced pattern recognition. The proposed method provides a robust learning framework suitable for dynamic environments where data distributions are constantly changing. The algorithm's adaptive nature, guided by quantum interference, aims to maintain high accuracy even as the data shifts, offering a significant advancement over static kernel methods. We present a theoretical framework outlining the algorithm's operation and discuss its potential for various applications.

DOI: 10.5281/zenodo.22317747

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